All Competitors

Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cellular trajectories over time.

Functional diffusion model that predicts single-cell perturbation responses by generating over distributions embedded in a Hilbert space, capturing population-level response variability.

Distributional flow matching model for single-cell perturbation prediction that models population-level expression shifts using a graph-aware differential-attention transformer.

A generative pre-trained transformer for single-cell multi-omics, pretrained on 33 million human cells for cell annotation, batch correction, and perturbation prediction.

Disentangled VAE framework for joint batch correction, condition-key-gene detection, and perturbation prediction in multi-batch multi-condition scRNA-seq data.